Positron emission tomography (PET) and single photon emission computed tomography (SPECT) are medical imaging techniques used to observe metabolic processes in the body. Both techniques detect gamma rays emitted indirectly by the decay of a radionuclide (tracer) which is introduced into the body on a biologically active molecule. PET tracers employ radioactive isotopes that emit positrons that secondarily decay through complete annihilation with electrons in the media: emitting two photons traveling in opposite directions. SPECT tracers employ radioactive isotopes that upon decay emits a single photon. Both PET and SPECT imaging are referred to as “nuclear medicine”. Three-dimensional images (reconstructions) of the concentrations of the tracer within the body are then constructed by a computer program. The final images depict physiological processes that can predict the presence of disease.
One of the most confounding artifacts in nuclear medicine is attenuation. Attenuation occurs when gamma rays are absorbed within the patient prior to detection by the imaging system. Attenuation artifact reduces clinical confidence, alters quantitative accuracy and can lead to misdiagnoses. To perform attenuation correction, two datasets are required: a map of the patient's anatomy (the attenuation map) and the uncorrected emission data. The attenuation map can be acquired either with a computed tomography (CT) scan or an external radioactive source. The emission data and attenuation map can be combined in a mathematical algorithm to compensate for the influence of attenuation. In most instances, the emission data and attenuation map are acquired sequentially. Patient motion between the emission data and attenuation map can lead to incomplete or inaccurate attenuation correction.
Image registration is the process of aligning the emission data and attenuation maps prior to performing attenuation correction. Misregistration of the transmission and emission datasets is a primary source of inaccurate attenuation correction in PET and SPECT images. For conventional reconstruction approaches, correction for misregistration must be performed prior to emission reconstruction. This requires the raw unreconstructed and uncorrected “sinogram” data be available. Most modern PET/CT and SPECT systems do not export a raw sinogram dataset that can be used for reconstruction and therefore if inaccurate misregistration correction has been applied, users would have to reprocess the data at the acquisition workstation. If the raw sinogram data is lost, deleted, or unavailable, studies with inaccurate misregistration correction are unusable clinically.
Another source of artifact is the presence of image noise. Image noise occurs when the random variations in the signal can be perceived by the clinician. Though this can be overcome by increasing the amount of radiation utilized, this comes at the expense of increasing the patient's overall radiation exposure, and thereby increasing the possibility of inducing future cancers. Most nuclear medicine imaging protocols require using the minimum radiation dose for performing a clinical task. To reduce image noise, the image data can be smoothed with image filters to preferentially reduce image noise while preserving the features of the image.
Most image noise filters rely on a simple spatial or frequency based filtering kernel that does not take into account the inherent feature of the image. This has the result of sacrificing image contrast to obtain greater degrees of noise control. This loss of contrast can result in masking true physiological defects and potentially missing disease. Most image processing programs employ either a frequency based noise filter, such as a low pass filter that preferentially preserves large objects in the image or a spatial based filter that performs weighted averaging across an image. More sophisticated filters can use an artificial intelligence (AI) technique that allows the program to enhance desirable features of an image while suppressing less desirable features. These AI-based filters, though useful, often require large amounts of computer processing power and are not easily translated onto conventional computer workstations.
This background discussion is intended to provide information related to the present invention which is not necessarily prior art.